Abnormality detection system, semiconductor device manufacturing system and semiconductor device manufacturing method

ABSTRACT

To provide an abnormality detection system capable of reducing work load of an engineer. An algorithm storage unit stores therein a detection algorithm corresponding to identification information of a detection target. An abnormality detection unit detects an abnormality in a detection target signal obtained from a monitor signal of the detection target using a corresponding detection algorithm in the algorithm storage unit. A detection target identification unit determines whether the detection algorithm corresponding to the identification information of the detection target is stored in the algorithm storage unit, and issues a generation request when it is not stored therein. An algorithm generation unit generates the detection algorithm using a corresponding detection target signal according to the generation request.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/895,419, filed on Feb. 13, 2018, which claims the benefit of JapanesePatent Application No. 2017-075650 filed on Apr. 6, 2017 including thespecification, drawings and abstract are incorporated herein byreference in their entirety.

BACKGROUND

The present invention relates to an abnormality detection system, asemiconductor device manufacturing system, and a semiconductor devicemanufacturing method, and specifically to a technology of detecting anabnormality of a manufacturing device or the like.

For example, Japanese Unexamined Patent Application Publication No.2006-278547 discloses an abnormality detection system capable ofavoiding an erroneous detection of an abnormality in a processingdevice. In the abnormality detection system, an abnormality detectionserver detects an abnormality of the processing device using either abasic algorithm having a plurality of parameters or a temporaryalgorithm excluding a parameter that temporarily varies according tomaintenance work.

SUMMARY

In recent years, with the fourth industrial revolution, suchtechnologies as AI (Artificial Intelligence) and IoT (Internet ofThings) have been increasingly applied to the manufacturing system inorder to improve manufacturing efficiency. Using such a manufacturingsystem makes it possible to, for example, monitor a processing state ofthe manufacturing device in real time using various sensors and detectan abnormality of the manufacturing device promptly based on themonitoring result.

For detecting the abnormality, for example, as disclosed in JapaneseUnexamined Patent Application Publication No. 2006-278547, it isfeasible to register a plurality of detection algorithms in a storagedevice in advance, select any one of the detection algorithms, anddetect the abnormality of a detection target based on the selecteddetection algorithm. With such a scheme, however, in a case where a newdetection algorithm is required for a new detection target, an engineergenerally needs to determine the detection algorithm while repeatingprototyping of a product using the manufacturing device isolated from amass production line and register the detection algorithm in the storagedevice.

Embodiments described below have been made in light of the aboveproblems, and other problems and new features will become apparent fromthe following description and accompanying drawings.

An abnormality detection system according to one embodiment includes analgorithm storage unit, an abnormality detection unit, a detectiontarget identification unit, and an algorithm generation unit. Thealgorithm storage unit stores therein detection algorithms correspondingto identification information of a detection target. The abnormalitydetection unit detects an abnormality in a detection target signalobtained from a monitor signal of the detection target using acorresponding detection algorithm in the algorithm storage unit. Thedetection target identification unit determines whether the detectionalgorithm corresponding to the identification information of thedetection target is stored in the algorithm storage unit and issues ageneration request when it is not stored therein. The algorithmgeneration unit generates a detection algorithm using a correspondingtarget signal according to the generation request.

The one embodiment makes it possible to reduce workload of the engineer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram showing an exemplary configuration of amain part of an abnormality detection system according to a firstembodiment of the present invention;

FIG. 2 is a flowchart showing an example of processing details of adetection target identification unit shown in FIG. 1;

FIG. 3 is a flowchart showing an example of processing details of anabnormality detection unit shown in FIG. 1;

FIG. 4 is a flowchart showing an example of processing details of analgorithm generation unit shown in FIG. 1;

FIG. 5 is a supplementary diagram of FIG. 5;

FIG. 6 is a schematic diagram showing an exemplary configuration of themain part of the abnormality detection system according to a secondembodiment of the present invention;

FIG. 7 is a diagram showing an exemplary structure of a main part of apacket communicated by a data identification unit in the abnormalitydetection system shown in FIG. 6;

FIG. 8 is a supplementary diagram of FIG. 7;

FIG. 9 is a diagram showing a specific example of the packet based onFIGS. 7 and 8;

FIG. 10 is a schematic diagram showing an exemplary configuration of themain part of a semiconductor device manufacturing system according to athird embodiment of the present invention;

FIG. 11A is a timing chart schematically showing an example of asemiconductor device manufacturing method using the manufacturing systemshown in FIG. 10;

FIG. 11B is a timing chart that follows FIG. 11A; and

FIG. 12 is a schematic diagram showing an exemplary configuration of themain part of the abnormality detection system according to a comparisonexample of the present invention.

DETAILED DESCRIPTION

Although the present invention will be described below in separatesections or embodiments as needed, they are not irrelevant to oneanother, but one may be a variation, detail, or supplementaryexplanation of a part or all of the other unless otherwise expresslystated. When referring to a number (including a number of pieces, anumerical value, an amount, and a range) in the following embodiments,it is not limited to the specific number but may be more than or lessthan the specific number unless otherwise expressly stated or unlessapparently limited to the specific number in principle.

Furthermore, it is needless to say that components (including elementsteps) in the following embodiments may not necessarily be essentialunless otherwise expressly stated or unless apparently necessary inprinciple. Similarly, in the following embodiments, when referring to ashape, a positional relation or the like of the components, what isapproximate to or similar to the shape is substantially included unlessotherwise expressly stated or unless apparently not applicable inprinciple. This similarly applies to the numeral values and rangesdescribed above.

Hereinbelow, embodiments of the present invention will be described withreference to drawings. It should be noted that like components aredenoted by like numerals throughout the drawings and the explanationthereof may not be repeated.

First Embodiment Configuration of Abnormality Detection System

FIG. 1 is a schematic diagram showing an exemplary configuration of amain part of an abnormality detection system according to a firstembodiment of the present invention. The abnormality detection system(abnormality detection device) shown in FIG. 1 includes a signal inputunit IIF, a target signal selection unit SS, an abnormality detectionunit EDT, a detection target identification unit TGR, an algorithmgeneration unit ALG, and an algorithm storage unit ADB. A case ofdetecting an abnormality of a manufacturing device in a semiconductordevice manufacturing system is assumed herein as an example of theabnormality detection system. It should be noted, however, that theinvention is not limited to such a case, but the abnormality detectionsystem is applicable as a system that detects an abnormality of variousproduction devices in various production systems.

The signal input unit IIF receives a monitor signal MS from a detectiontarget, performs a predetermined signal processing, and transmits theresulting signal to the target signal selection unit SS. The monitorsignal MS is indicative of, for example, a processing state of themanufacturing device, which is a sensor signal from various sensorsprovided in the manufacturing device or added to the manufacturingdevice. The various sensors may be any sensors such as, for example, aflow sensor that monitors a flow rate of a gas, a pressure sensor thatmonitors a pressure in a chamber, a power sensor that monitors an RFpower of plasma, and an EPD (End Point Detector) that monitors aprogress of etching.

In the semiconductor device manufacturing system, the sensor signal canbe communicated between devices using a communication protocol calledSECS (SEMI Equipment Communications Standard) . As a physical interfaceof the SECS, RS232 or Ethernet (registered trademark) may be used. Thesignal input unit IIF takes a role of, for example, a communicationinterface of the SECS. In such a case, the signal input unit IIFreceives a sensor signal transmitted from a sensor using the SECS, forexample. The signal input unit IIF may also include, for example, ananalog-digital conversion circuit. In this case, the signal input unitIIF directly receives an analog signal from the sensor as the monitorsignal MS without using the SECS, converts the analog signal to adigital signal, and transmits the digital signal to the target signalselection unit SS.

The target signal selection unit SS determines a detection target signalTS as a detection target from among the monitor signals MS received viathe signal input unit IIF, and stores the detection target signal TS ina detection target signal buffer SBF. The target signal selection unitSS transmits the detection target signal TS stored in the detectiontarget signal buffer SBF to the abnormality detection unit EDT and thealgorithm generation unit ALG. For example, in a case where the sensoris constantly in operation, the monitor signal MS may include a signalof an unnecessary section (for example, an idle section in which themanufacturing device is not performing any practical operation). Thetarget signal selection unit SS determines a detection target section inwhich the manufacturing device is substantially in operation from amongthe monitor signals MS, and extracts a signal of the section as thedetection target signal TS. Specifically, for example, when themonitoring signal MS presents 0V in the idle section, the target signalselection unit SS specifies the detection target section as a section inwhich the voltage level of the monitor signal MS is 0.1 V or higher.

The algorithm storage unit ADB stores therein a plurality of detectionalgorithms AL[1] to AL[n] corresponding to identification information DIof the detection target. Herein, the plurality of detection algorithmsAL[1] to AL[n] are collectively referred to as a detection algorithm AL.The abnormality detection unit EDT detects an abnormality of thedetection target signal TS using the detection algorithm ALcorresponding to the identification information DI stored in thealgorithm storage unit ADB, and transmits an output signal OUT as adetection result. When the abnormality is detected, an alarm display ona monitor or the like, an abnormality notification to another controlequipment, a lighting to notify detection of the abnormality, or thelike may be activated based on the output signal OUT.

The detection algorithm AL may include an algorithm based on the AI, analgorithm based on a statistical approach, and the like. The AI basedalgorithm may use, for example, a model of a neural network that haslearned a feature of the detection target signal TS. The model allowsfor generating, for example, an expected value signal (i.e., idealdetection target signal) by receiving the detection target signal TS andreflecting the learned feature on the received signal. The abnormalitydetection unit EDT determines the presence of the abnormality in thedetection target signal TS based on whether a difference between thedetection target signal TS and the expected value signal is within anacceptable range. On the other hand, the algorithm based on thestatistical approach uses a normal distribution model reflecting variousstatistical values of the detection target signal TS, a multivariateanalysis model, or the like. The abnormality detection unit EDTdetermines the presence of the abnormality of the abnormality in thedetection target signal TS based on whether a variation or the like ofthe detection target signal TS is within a range statistically(logically) regarded to be normal using these models.

The detection target identification unit TGR receives the identificationinformation DI of the detection target, and determines whether thedetection algorithm AL corresponding to the identification informationDI is stored in the algorithm storage unit ADB. The detection targetidentification unit TGR transmits selection information SI to theabnormality detection unit EDT when the detection algorithm AL is storedin the algorithm storage unit ADB. The selection information SI is usedto identify the detection algorithm AL corresponding to theidentification information DI. The abnormality detection unit EDTobtains the detection algorithm AL corresponding to the identificationinformation DI from the algorithm storage unit ADB based on theselection information SI and stores the detection algorithm AL in adetection algorithm buffer ABF, thereby performing an abnormalitydetection based on the detection algorithm AL.

On the other hand, when the detection algorithm AL corresponding to theidentification information DI is not stored in the algorithm storageunit ADB, the detection target identification unit TGR transmits anunsupported notification NN to the abnormality detection unit EDT andissues a generation request GR to the algorithm generation unit ALG. Thealgorithm generation unit ALG includes, for example, the identificationinformation DI. When receiving the unsupported notification NN, theabnormality detection unit EDT does not detect an abnormality in thedetection target signal TS.

The algorithm generation unit ALG generates the detection algorithm ALcorresponding to the identification information DI included in thegeneration request GR using the detection target signal TS from thetarget signal selection unit SS. When generation of the detectionalgorithm AL is completed, the algorithm generation unit ALG issues ageneration completion notification ED and stores the generated detectionalgorithm AL in the algorithm storage unit ADB. For example, an engineerrecognizes that the abnormality detection based on the detectionalgorithm AL by the abnormality detection unit EDT has become possibleby receiving the generation completion notification ED via an e-mail orthe like.

For example, in the semiconductor device manufacturing system, amanagement device can transmit the identification information DIcontaining a recipe ID to the manufacturing device or the like using theSECS. The recipe ID is an ID for identifying a manufacturing conditionof the manufacturing device. The recipe ID is used to identify detailedprocess condition such as, for example, a type of a gas used, a flowrate of the gas, a processing time, or the like. The identificationinformation DI includes, in addition to the recipe ID, a plurality ofcondition parameters such as information about the semiconductor device(product) to be processed, information about the manufacturing device,and the like.

For example, the detection target identification unit TGR specifies thedetection algorithm AL by appropriately combining a plurality ofcondition parameters in the identification information DI by apredetermined method, and transmits the selection information SI. Inthis case, the detection target identification unit TGR issues thegeneration request GR basically when a value of any one of the pluralityof condition parameters included in the predetermined combinationchanges. However, the combination is not limited to an AND condition ofthe plurality of condition parameters but other conditions such as an ORcondition and a DON'T CARE condition may be used, and the generationrequest GR may not always be issued even when any of the values changes.The detection algorithm AL and the recipe ID generally correspondone-to-one, but a single detection algorithm AL may correspond to aplurality of recipe IDs depending on the condition setting.

In FIG. 1, the signal input unit IIF is implemented by a dedicatedcircuit, by program processing performed by a CPU (Central ProcessingUnit), or by a combination thereof. The target signal selection unit SS,the abnormality detection unit EDT, the detection target identificationunit TGR, and the algorithm generation unit ALG are implemented mainlyby program processing performed by the CPU. Each of the detection targetsignal buffer SBF and the detection algorithm buffer ABF is configuredby a RAM (Random Access Memory). The algorithm storage unit ADB isconfigured by a storage device such as a non-volatile memory and an HDD(Hard Disk Drive). It should be noted, however, that the implementationof each unit is not limited to the examples described above but may beany one of hardware, software, and a combination of hardware andsoftware.

Moreover, the abnormality detection system (abnormality detectiondevice) can be configured by, for example, a single component (forexample, a wiring board) having a microcomputer including a CPU mountedthereon. Namely, it is possible to mount each unit shown in FIG. 1 on asingle microcomputer, or isolate the algorithm storage unit ADB andmount it on an external component (for example, a flash memory) of themicrocomputer. Furthermore, the abnormality detection system may beprovided to the manufacturing device on a one-to-one basis or only oneabnormality detection system may be provided to a plurality ofmanufacturing devices. In a case of providing it on the one-to-onebasis, for example, the abnormality detection system may be incorporatedin the manufacturing device, or installed as an external component ofthe manufacturing device.

Detailed Operation of Each Unit

FIG. 2 is a flowchart showing an example of processing details of thedetection target identification unit shown in FIG. 1. In FIG. 2, thedetection target identification unit TGR repeats the followingprocessing as long as it is in an enabled state (Step S101). Thedetection target identification unit TGR firstly continues to wait foran update of the identification information DI of the detection target(Step S102). The manufacturing device performs processing ofsequentially input semiconductor devices (semiconductor wafers) based onthe currently set identification information DI unless theidentification information DI is updated by the management device. As aresult, the detection target section is generated in the monitor signalMS every time each semiconductor wafer is processed. The target signalselection unit SS extracts the monitor signal MS of the detection targetsection as the detection target signal TS and transmits the extracteddetection target signal TS every time each semiconductor wafer isprocessed.

If the identification information DI is updated at Step S102, thedetection target identification unit TGR determines whether thedetection algorithm AL corresponding to the identification informationDI is already stored in the algorithm storage unit ADB (Step S103). Ifit is already stored in the algorithm storage unit ADB, the detectiontarget identification unit TGR transmits the selection information SIcorresponding to the updated identification information DI to theabnormality detection unit EDT (Step S104). On the other hand, if it isnot stored in the algorithm storage unit ADB, the detection targetidentification unit TGR transmits the unsupported notification NN to theabnormality detection unit EDT (Step S105), and issues the generationrequest GR to the algorithm generation unit ALG (Step S106).

FIG. 3 is a flowchart showing an example of processing details of theabnormality detection unit shown in FIG. 1. In FIG. 3, the abnormalitydetection unit EDT repeats the following processing as long as it is inan enabled state (Step S201). If the abnormality detection unit EDTreceives the unsupported notification NN from the detection targetidentification unit TGR (Step S202), the abnormality detection unit EDTfirstly validates an unsupported flag and returns to Step S201 (StepS203). On the other hand, if the abnormality detection unit EDT receivesthe selection information SI from the detection target identificationunit TGR (Step S204), the abnormality detection unit EDT invalidates theunsupported flag (Step S205), duplicates the detection algorithm ALspecified by the selection information SI from the algorithm storageunit ADB on the detection algorithm buffer ABF, and then returns to StepS201 (Step S206).

Subsequently, the abnormality detection unit EDT returns to Step S201 ifthe unsupported flag is valid, or proceeds to Step S208 if it is invalid(Step S207). At Step S208, the abnormality detection unit EDT waits forreceiving the detection target signal TS from the target signalselection unit SS while dealing with the update of the unsupportednotification NN (Step S202) and the selection information SI (StepS204). If the abnormality detection unit EDT receives the detectiontarget signal TS, it determines the presence of an abnormality in thedetection target signal TS based on the detection algorithm AL in thedetection algorithm buffer ABF (Step S209). If the abnormality detectionunit EDT detects an abnormality at Step S209 (Step S210), it transmitsthe output signal OUT containing the abnormality detection result (StepS211).

FIG. 4 is a flowchart showing an example of processing details of thealgorithm generation unit shown in FIG. 1. FIG. 5 is a supplementarydiagram of FIG. 5. In FIG. 4, the algorithm generation unit ALG repeatsthe following processing as long as it is in an enabled state (StepS301). The algorithm generation unit ALG firstly continues to wait forthe generation request GR from the detection target identification unitTGR (Step S302). If the algorithm generation unit ALG receives thegeneration request GR, the algorithm generation unit ALG receives thedetection target signal TS from the target signal selection unit SS(Step S303), and generates the detection algorithm AL reflecting thedetection target signal TS (Step S304). Steps S303 and S304 are repeateduntil generation of the detection algorithm AL is completed (Step S305).

For example, when performing the abnormality detection based on deeplearning that is a type of AI, a network structure, a weight, a biasvalue, and the like of the neural network are generated as the detectionalgorithm AL. Deep learning includes repetition of learning processingof, while sequentially inputting the detection target signals TS to theneural network, calculating a difference between a value estimated bythe neural network and an expected value as a loss value, and feedingback the loss value to the weight and the bias so as to make the lossvalue smaller. It is thus possible to determine whether generation ofthe detection algorithm AL is completed based on the convergence of theloss value.

For example, FIG. 5 shows an example of a relation between the number oflearning times and the loss value, in which a threshold value Lth fordetermining whether generation of the detection algorithm AL iscompleted is provided with respect to the loss value. When the lossvalue is no higher than the threshold value Lth, the algorithmgeneration unit ALG determines that generation of the detectionalgorithm AL is completed. It should be noted that, when using thedetection algorithm AL based on the statistical approach, the algorithmgeneration unit ALG may determine whether generation of the detectionalgorithm AL is completed by, for example, determining whether thenumber of receiving times of the detection target signal TS (i.e.,parameter) has reached a predetermined number.

When generation of the detection algorithm AL is completed, thealgorithm generation unit ALG stores the generated detection algorithmAL in the algorithm storage unit ADB (Step S306) . At this time, thedetection algorithm AL is stored in the algorithm storage unit ADB asbeing linked to the identification information DI included in thegeneration request GR, for example. Moreover, the algorithm generationunit ALG issues the generation completion notification ED (Step S307).

Main Effects of First Embodiment

FIG. 12 is a schematic diagram showing an exemplary configuration of themain part of the abnormality detection system according to a comparisonexample of the present invention. As shown in FIG. 12, in theabnormality detection system according to the comparison example,abnormality detection is performed using an abnormality detection serverarranged in the manufacturing system. That is, each monitor signal froma plurality of manufacturing devices in the manufacturing system isaggregated to the abnormality detection server via a communicationnetwork, where abnormality detection is performed. The abnormalitydetection server includes a data storage device MEM that stores thereina plurality of detection algorithms AL′[1], AL′[2] and a CPU. The CPUincludes an abnormality detection unit EDT′ implemented by programprocessing. The abnormality detection unit EDT′ detects an abnormalityin the monitor signal while appropriately selecting any one of theplurality of detection algorithms AL′[1], AL′[2].

A case is now assumed in which the abnormality detection systemaccording to the comparison example is used and a new detection target(for example, a combination of the manufacturing device and a product)emerges. In this case, an engineer sequentially inputs prototypesemiconductor devices (semiconductor wafers) to the manufacturing deviceof the detection target, and simultaneously downloads the monitorsignals during the input period to, for example, his own PC (PersonalComputer). The engineer may generate a new detection algorithm to detectan abnormality in the monitor signal and registers it in the datastorage device MEM using the PC.

In this case, however, the workload of the engineer may be heavier.Moreover, inputting such a prototype semiconductor device usuallyrequires a procedure different from a batch processing procedure for amass production line for inputting a mass production semiconductordevice, and thus may cause a situation in which the manufacturing deviceof the detection target is isolated from the mass production line (inother words, exclusive use of the manufacturing device) for a certainperiod. This may possibly reduce the manufacturing efficiency.

Furthermore, for example, when the detection algorithms AL′[1], AL′[2]are AI-based detection algorithms, as can be seen from FIG. 5, it isdifficult for the engineer to explicitly recognize how many prototypesemiconductor devices (semiconductor wafers) should be input to generatethe detection algorithm. As a result, generation of the detectionalgorithm is performed on a trial-and-error basis, which may lead tocost increase due to excessive input of the prototype semiconductordevices and time loss as a result of repeating the input on atrial-and-error basis.

On the other hand, using the abnormality detection system shown in FIG.1, when a new detection target emerges, for example, the detectionalgorithm AL is automatically generated by the algorithm generation unitALG by inputting the prototype semiconductor device to the manufacturingdevice of the detection target, and the detection algorithm AL isautomatically stored in the algorithm storage unit ADB. This makes itpossible to reduce the workload of the engineer. Moreover, theabnormality detection system shown in FIG. 1 is configured so that theprototype semiconductor device and the mass production semiconductordevice for which the detection algorithm AL has already been generatedcan be input without distinguishing one from the other. For example, theabnormality detection unit EDT can switch whether to execute abnormalitydetection or not depending on the selection information SI and theunsupported notification NN. As a result, as shown in FIG. 12, theexclusive use of the manufacturing device is not caused and thusreduction of the manufacturing efficiency can be suppressed.

Furthermore, because the abnormality detection system shown in FIG. 1issues the generation completion notification ED when generation of thedetection algorithm AL is completed, when generating the detectionalgorithm, it may continue to input the prototype semiconductor devicesby mixing them into the mass production semiconductor devices, forexample, until the generation completion notification ED is issued. Ifthe generation completion notification ED is issued, it is possible tomanually stop the input of the prototype semiconductor device and tostop the input of the prototype semiconductor device by an automaticprocessing using the management device. This also makes it possible toreduce the above-mentioned cost loss and time loss.

It should be noted that the abnormality detection system shown in FIG. 1can be linked one-to-one to the manufacturing device as the abnormalitydetection device. In such a case, abnormality detection can be performedquicker than a case in which the signals are aggregated to theabnormality detection server as shown in FIG. 12.

Second Embodiment Configuration of Abnormality Detection System(Application Example)

FIG. 6 is a schematic diagram showing an exemplary configuration of themain part of the abnormality detection system according to a secondembodiment of the present invention. The abnormality detection system(abnormality detection device) shown in FIG. 6 includes an abnormalitydetection execution device DEVE, an algorithm generation device DEVG,and a communication network NW that couples them. The abnormalitydetection execution device DEVE is configured by, for example, a singledevice including a microcomputer (for example, a wiring board), and thealgorithm generation device DEVG is configured by another device. Thealgorithm generation device DEVG may be, for example, a computer systemsuch as a PC.

The abnormality detection execution device DEVE includes thosecomponents in the exemplary configuration shown in FIG. 1 except thealgorithm generation unit ALG, and additionally includes a dataidentification unit DR1. On the other hand, the algorithm generationdevice DEVG includes the algorithm generation unit ALG among theexemplary configuration shown in FIG. 1, and additionally includes adata identification unit DR2. Each of the data identification units DR1,DR2 takes a role of an interface of the communication network NW andcommunicates with each other via the communication network NW. Thecommunication network NW may be, for example, an Ethernet (registeredtrademark) network.

The data identification unit DR1 receives the identification informationDI from the communication network NW and transmits it to the detectiontarget identification unit TGR. Moreover, the data identification unitDR1 transmits the generation request GR, the detection target signal TS,and the output signal OUT from the detection target identification unitTGR, the target signal selection unit SS, and the abnormality detectionunit EDT to the communication network

NW. Furthermore, the data identification unit DR1 receives the detectionalgorithm (detection parameter) AL from the communication network NW(algorithm generation device DEVG) and stores it in the algorithmstorage unit ADB.

The data identification unit DR2 receives the generation request GR andthe detection target signal TS from the communication network NW(abnormality detection execution device DEVE) and transmits them to thealgorithm generation unit ALG. The data identification unit DR2 alsotransmits the detection algorithm (detection parameter) AL generated bythe algorithm generation unit ALG and the generation completionnotification ED to the abnormality detection execution device DEVE orthe like via the communication network NW. It should be noted that theremay be provided a plurality of algorithm generation units ALG, which maygenerate different detection algorithms AL in parallel.

Structure of Communication Format

FIG. 7 is a diagram showing an exemplary structure of a main part of apacket communicated by the data identification unit in the abnormalitydetection system shown in FIG. 6, and FIG. 8 is a supplementary diagramof FIG. 7. As shown in FIG. 7, each packet communicated by the dataidentification units DR1, DR2 contains three elements of a packet typeTYP, a size SZ, and a payload PLD. The packet type TYP stores thereinany one of the numbers shown in FIG. 8. The detection target signal TS,the output signal (detection result) OUT, the identification informationDI of the detection target, the generation request GR of the detectionalgorithm, and the detection algorithm (detection parameter) AL aredistinguished from one another by this number. The size SZ storestherein the data size of the payload PLD. For example, in a case wherethe data size of the payload PLD is 8 bytes, a value ‘8’ is stored inthe size SZ. The payload PLD stores therein the data corresponding tothe packet type TYP.

FIG. 9 is a diagram showing a specific example of the packet based onFIGS. 7 and 8. To transmit the detection target signal TS to thecommunication network NW, the data identification unit DR1 generates andtransmits a packet PK1. The packet PK1 stores ‘1’ in the packet typeTYP, ‘80’ in the size SZ, and an 80-byte data of “1, 2, 7, 10, . . . ”to be the detection target signal TS in the payload PLD. The dataidentification unit DR2 receives the packet PK1, and transmits the dataof the payload PLD to the algorithm generation unit ALG.

To transmit the output signal (detection result) OUT to thecommunication network NW, the data identification unit DR1 generates andtransmits a packet PK2. The packet PK2 stores ‘2’ in the packet typeTYP, ‘2’ in the size SZ, and a character code of “OK” indicating that noabnormality is detected in the payload PLD. The packet PK2 may bereceived by, for example, a SCADA (Supervisory Control And DataAcquisition) (not shown) that monitors the system.

The data identification unit DR1 receives a packet PK3 containing theidentification information DI generated by, for example, a MES(Manufacturing Execution System) (not shown) that manages a productionprocess via the communication network NW. The packet PK3 stores ‘3’ inthe packet type TYP, ‘7’ in the size SZ, and a character code of“Recipe1” as the identification information DI in the payload PLD. Asdescribed with reference to FIG. 1, the identification information DImay also include various condition parameters, but it is assumed hereinthat the identification information DI is the recipe ID for convenienceof explanation. The data identification unit DR1 transmits the data ofthe payload PLD to the detection target identification unit TGR.

To transmit the generation request GR to the communication network NW,the data identification unit DR1 generates and transmits a packet PK4.The packet PK4 stores ‘4’ in the packet type TYP, ‘7’ in the size SZ,and a character code of “Recipe1” as the identification information DIcorresponding to the generation request GR in the payload PLD. The dataidentification unit DR2 receives the packet PK4, and issues thegeneration request GR linked to the identification information DI to thealgorithm generation unit ALG. It should be noted that, in a case wherea plurality of algorithm generation units ALG are provided, the dataidentification unit DR2 can have different algorithm generation unitsALG generate detection algorithms in parallel when it receives thepackets PK4 containing different identification information DI within apredetermined period.

To transmit the detection algorithm (detection parameter) AL generatedby the algorithm generation unit ALG to the communication network NW,the data identification unit DR2 generates and transmits a packet PK5.The packet PK5 contains two pairs of the size SZ and the payload PLD.The packet type TYP stores therein ‘5’. The size SZ of the first pairstores therein ‘7’ and the payload PLD of the first pair stores thereinthe character code of “Recipe1” as the identification information DIcorresponding to the generated detection algorithm AL.

The size SZ of the second pair stores therein ‘XX’ and the payload PLDof the second pair stores therein the detection parameter of thegenerated detection algorithm AL (for example, the network structure ofthe neural network, the weight, or the bias value). The dataidentification unit DR1 receives the packet PKS, links the detectionalgorithm (detection parameter) AL to the identification information DI,and registers it in the algorithm storage unit ADB. In the packet PKS,for example, a packet in which the size SZ and the payload PLD in thesecond pair are deleted can be used as the generation completionnotification ED shown in FIG. 1.

Main Effect of Second Embodiment

It is possible to obtain the same effect as the first embodiment byusing the abnormality detection system according to the secondembodiment. Furthermore, in the second embodiment, a configurationsuitable for an actual practice is obtained by separating theabnormality detection execution device DEVE and the algorithm generationdevice DEVG to implement the abnormality detection system shown inFIG. 1. For example, a PC having a high computing capability can be usedas the algorithm generation device DEVG assuming deep learning and thelike, and a microcomputer having a small size and low power consumptioncan be used as the abnormality detection execution device DEVE.

Thus, by combining the low power consumption abnormality detectionexecution device DEVE that operates constantly in association withabnormality detection and the algorithm generation device DEVG thatoperates only when an unknown detection target emerges, the powerconsumption of the whole system can be reduced. Moreover, it is possibleto reduce time required to calculate a detection parameter when theunknown detection target emerges. Furthermore, by configuring theabnormality detection execution device DEVE using a small microcomputerinstead of a large one like a PC, possibility of installing theabnormality detection execution device DEVE in a limited installationspace can be increased. It should be noted that the communication formatis not limited to such a format as shown in FIGS. 7 and 8 but a similarfunction can be achieved using the SECS.

Third Embodiment Configuration of Semiconductor Device ManufacturingSystem

FIG. 10 is a schematic diagram showing an exemplary configuration of themain part of a semiconductor device manufacturing system according to athird embodiment of the present invention. The manufacturing systemshown in FIG. 10 includes a plurality of abnormality detection executiondevices DEVEa, DEVEb, a plurality of manufacturing devices (detectiontarget devices) MEa, MEb, the algorithm generation device DEVG, the MES(Manufacturing Execution System), the SCADA (Supervisory Control AndData Acquisition), and the communication network NW that couples them.

Each of the abnormality detection execution devices DEVEa, DEVEbincludes the same configuration as and performs the same operation asthe abnormality detection execution device DEVE shown in FIG. 6. Thealgorithm generation device DEVG also includes the same configuration asand performs the same operation as shown in FIG. 6. It should be noted,however, that algorithm generation device DEVG includes a plurality of(two in this embodiment) algorithm generation units ALG[1], ALG[2] asdescribed with reference to the second embodiment.

The SCADA is a monitoring device for the whole manufacturing system. TheMES is a management device for the production process, which transmitsthe identification information DI including the recipe ID indicative ofthe manufacturing condition of the manufacturing devices MEa, MEb to thecommunication network NW when inputting the semiconductor device(semiconductor wafer) to the manufacturing devices MEa, MEb. Each of themanufacturing devices MEa, MEb processes the semiconductor wafer underthe manufacturing condition based on the recipe ID from the MES, andoutputs the monitor signal MS indicative of the processing state. Themanufacturing devices MEa, MEb herein output the monitor signal MS tothe abnormality detection execution devices DEVEa, DEVEb, respectively.Examples of the manufacturing devices MEa, MEb include, for example, aplasma CVD (Chemical Vapor Deposition) device that performs a processingtreatment associated with a film forming process, an exposure devicethat performs a processing treatment associated with a patterningprocess, and a plasma etching device that performs a processingtreatment associated with an etching process.

Semiconductor Device Manufacturing Method

FIG. 11A is a timing chart schematically showing an example of asemiconductor device manufacturing method using the manufacturing systemshown in FIG. 10, and FIG. 11B is a timing chart that follows FIG. 11A.At Step S401 in FIG. 11A, the MES transmits a packet PK31 to thecommunication network NW. The packet PK31 notifies the manufacturingdevice MEa and the abnormality detection execution device DEVEa of theidentification information DI of “Recipe1”. Here, although theidentification information DI actually includes a plurality of conditionparameters, it is assumed herein as the recipe ID for convenience ofexplanation as in the second embodiment. In the abnormality detectionexecution device DEVEa of this example, the detection algorithm ALcorresponding to “Recipe1” is already stored in the algorithm storageunit ADB. In this case, the packet PK31 means an order of commencementusing amass production semiconductor wafer.

The manufacturing device MEa processes the sequentially input massproduction semiconductor wafers using “Recipe1”, and outputs the monitorsignal MS during the processing. The abnormality detection executiondevice DEVEa sequentially extracts detection target signals TS1, TS2from the monitor signal MS every time each of the sequentially inputsemiconductor wafers is processed. The abnormality detection executiondevice DEVEa detects an abnormality of the manufacturing device MEa bydetermining the presence of the abnormality in the detection targetsignals TS1, TS2 based on the detection algorithm AL corresponding to“Recipe1”. As a result, the abnormality detection execution device DEVEatransmits a packet PK21 indicative of the detection result of “OK” tothe SCADA when it determines that the detection target signal TS1 isnormal, and transmits a packet PK22 indicative of the detection resultof “NG” to the SCADA when it determines that the detection target signalTS2 is abnormal.

At Step S402, the MES then transmits a packet PK32 to the communicationnetwork NW. The packet PK32 notifies the manufacturing device MEa andthe abnormality detection execution device DEVEa of the identificationinformation DI of “Recipe2”. In the abnormality detection executiondevice DEVEa of this example, the detection algorithm AL correspondingto “Recipe2” is not stored in the algorithm storage unit ADB. In thiscase, the packet PK32 means the order of commencement using a prototypesemiconductor wafer.

The abnormality detection execution device DEVEa receives theidentification information DI of “Recipe2” and transmits a packet PK41indicative of the generation request GR of the detection algorithm ALcorresponding to “Recipe2” to the algorithm generation device DEVG.Accordingly, the algorithm generation device DEVG starts generation ofthe detection algorithm AL corresponding to “Recipe2” and waits for adetection target signal required for generation.

The manufacturing device MEa processes the input prototype semiconductorwafer using “Recipe2” and outputs the monitor signal MS during theprocessing. The abnormality detection execution device DEVEa extracts adetection target signal TS3 from the monitor signal MS. The abnormalitydetection execution device DEVEa does not detect an abnormality in thedetection target signal TS3 but transmits a packet PK11 containing thedetection target signal TS3 to the algorithm generation device DEVG. Thealgorithm generation device DEVG generates the detection algorithm ALreflecting the detection target signal TS3. It is assumed here that thedetection algorithm AL has been generated using the detection targetsignal TS3 alone.

When the detection algorithm AL corresponding to“Recipe2” is generated,the algorithm generation device DEVG transmits a packet PK51 to the MES,for example. The packet PK51 uses the packet PK5 as the generationcompletion notification ED, as described with reference to FIG. 9. TheMES recognizes that the mass production semiconductor wafercorresponding to “Recipe2” can be now input by receiving the packetPK51. Moreover, the algorithm generation device DEVG transmits a packetPK52 containing “Recipe2” and the detection algorithm (detectionparameter) to the abnormality detection execution device DEVEa. Theabnormality detection execution device DEVEa stores the detectionalgorithm AL corresponding to “Recipe2” in the algorithm storage unitADB according to the packet PK52.

At Step S403 in FIG. 11B, the MES transmits a packet PK33 to thecommunication network NW. The packet PK33 notifies the manufacturingdevice MEa and the abnormality detection execution device DEVEa of theidentification information DI of “Recipe3”. In the abnormality detectionexecution device DEVEa of this example, the detection algorithm ALcorresponding to “Recipe3” is not stored in the algorithm storage unitADB as in the case of Step S402. In this case, as in Step S402, thepacket PK33 means the order of commencement using a prototypesemiconductor wafer.

The abnormality detection execution device DEVEa transmits a packet PK42indicative of the generation request GR of the detection algorithm ALcorresponding to “Recipe3” to the algorithm generation device DEVG. Thealgorithm generation device DEVG starts generation of the detectionalgorithm AL corresponding to “Recipe3” according to the packet PK42,and waits for the detection target signal required for generation. Atthis time, if the algorithm generation unit ALG[1] in the algorithmgeneration device DEVG is ever generating the detection algorithm ALassociated with Step S402 described above, the algorithm generation unitALG[2] starts generation of the detection algorithm AL corresponding to“Recipe3”.

The manufacturing device MEa processes the input prototype semiconductorwafer using “Recipe3”, and the abnormality detection execution deviceDEVEa extracts a detection target signal TS4 from the monitor signal MSassociated with the processing. The abnormality detection executiondevice DEVEa does not detect an abnormality in the detection targetsignal TS4 but transmits a packet PK12 containing the detection targetsignal TS4 to the algorithm generation device DEVG. The algorithmgeneration device DEVG generates the detection algorithm AL reflectingthe detection target signal TS4. In this example, generation of thedetection algorithm AL is not completed, and the algorithm generationdevice DEVG continues to wait for the detection target signal associatedwith “Recipe3”.

At Step S404, the MES transmits a packet PK34 to the communicationnetwork NW. The packet PK34 notifies the manufacturing device MEa andthe abnormality detection execution device DEVEa of the identificationinformation DI of “Recipe2”. In the abnormality detection executiondevice DEVEa, the detection algorithm AL corresponding to “Recipe2” isstored in the algorithm storage unit ADB in association with Step S402described above. Thus, the packet PK34 means the order of commencementusing a mass production semiconductor wafer.

The manufacturing device MEa processes the input mass productionsemiconductor wafer using “Recipe2”, and the abnormality detectionexecution device DEVEa extracts a detection target signal TS5 from themonitor signal MS associated with the processing. The abnormalitydetection execution device DEVEa detects an abnormality of themanufacturing device MEa by determining the presence of an abnormalityin the detection target signal TS5 based on the detection algorithm ALcorresponding to “Recipe2”. As a result, the abnormality detectionexecution device DEVEa transmits a packet PK23 indicative of thedetection result of “OK” to the SCADA when it determines that thedetection target signal TS5 is normal.

Subsequently at Step S405, the same processing as in Step S401 isperformed. In other words, a packet PK35 performs the same processing on“Recipe1” as in the case of the packet PK31, and a packet PK24 similarto the packet PK21 described above is transmitted as the detectionresult. Although not shown, when a packet similar to the packet PK33 atStep S403 is transmitted later, the algorithm generation device DEVGresumes generation of the detection algorithm AL corresponding to“Recipe3” based on the subsequent detection target signal.

Main Effect of Third Embodiment

The same effects as those described in the first and second embodimentscan be obtained using the abnormality detection system according to thethird embodiment. Especially as illustrated in FIGS. 11A and 11B, it ispossible to generate the detection algorithm AL as in Steps S402, S403between Step S401 and Step S405 (i.e. between batch processings for themass production line), for example, without isolating the manufacturingdevice MEa from the mass production line. Moreover, as illustrated inSteps S402, S404, it is possible to quickly start mass production usingthe detection algorithm AL as its generation is completed. This makes itpossible to improve the manufacturing efficiency.

Furthermore, as shown in FIG. 10, providing the manufacturing devicesMEa, MEb and the abnormality detection execution devices DEVEa, DEVEb ona one-to-one basis makes it possible to detect an abnormality promptly.In other words, as shown in FIG. 12, by not aggregating the monitorsignals MS from the manufacturing devices MEa, MEb to the abnormalitydetection server coupled to the communication network NW but performingabnormality detection separately on each one of the manufacturingdevices MEa, MEb, a processing load and a communication load (i.e.resource) are dispersed, and consequently it is made possible to detectan abnormality promptly. It should be noted that the abnormalitydetection execution devices DEVEa, DEVEb are, as described above,configured by, for example, a small wiring board, which can beincorporated in the manufacturing devices MEa, MEb or installed asexternal components of the manufacturing devices MEa, MEb.

Although the invention made by the inventors has been specificallydescribed above with reference to the embodiments, the invention is notlimited to the embodiments, but various modifications can be madewithout departing from the scope of the invention. For example, theembodiments are made to describe the invention in detail for betterunderstanding but not limited to include all the configurationsdescribed above. It is possible to replace a part of a configuration ofone embodiment with a configuration of another embodiment or to add aconfiguration of one embodiment to a configuration of another. It isalso possible to add, delete, or replace a part of the configuration ofeach embodiment.

What is claimed is:
 1. A method for manufacturing of devices comprising:processing the devices in accordance with a manufacturing configuration;receiving a monitoring signal obtained by electrically sensing thestatus of the processing; generating an abnormality detection algorithmbased on the monitoring signal; and detecting an abnormality of theprocessing based on the abnormality detection algorithm.
 2. The methodaccording to claim 1, wherein the generating includes updating theabnormality detection algorithm.
 3. The method according to claim 1,wherein the receiving, the generating and the detecting are performedwhile the processing.
 4. The method according to claim 1, wherein theabnormality detection algorithm comprises parameters for neural network.5. The method according to claim 4, wherein the parameters arerepeatedly updated by feeding back learning results of the neuralnetwork.
 6. The method according to claim 1, further comprising:identifying the manufacturing condition from a plurality ofmanufacturing conditions; selecting the abnormality detection algorithmcorresponding to the identified manufacturing condition from a pluralityof abnormality detection algorithms, the generating is performed whenthe abnormality detection algorithm corresponding to the identifiedmanufacturing condition is not found at the selecting.
 7. The methodaccording to claim 6, wherein the generating includes updating theabnormality detection algorithm.
 8. The method according to claim 6,wherein the receiving, the generating, the detecting, the identifyingand the selecting are performed while the processing.
 9. The methodaccording to claim 6, wherein the abnormality detection algorithmcomprises parameters for neural network.
 10. The method according toclaim 9, wherein the parameters are repeatedly updated by feeding backlearning results of the neural network.
 11. A computing devicecomprising: a network interface circuit configured to inputidentification information which identifies a configuration ofprocessing; a signal input circuit configured to input a monitoringsignal which corresponds to the configuration; a storage circuitconfigured to store a detection algorithm which corresponds to theconfiguration; and a detection execution circuit configured to detectthe status of the processing by executing the detection algorithm withthe monitoring signal, wherein the detection algorithm is obtained byreceiving from an external algorithm generation device via the networkinterface.
 12. The computing device according to claim 11, wherein thedetection algorithm comprises parameters for neural network.
 13. Thecomputing device according to claim 12, wherein the parameters forneural network are learned at the external algorithm generation device.14. The computing device according to claim 11, wherein the detectionalgorithm comprises parameters for statistic algorithm.
 15. Thecomputing device according to claim 14, wherein the parameters forstatistic algorithm are collected at the external algorithm generationdevice.
 16. The computing device according to claim 11, wherein thecomputing device is configured to send a request for generating of thedetection algorithm to the external algorithm generation device via thenetwork interface, when the detection algorithm which corresponds to theconfiguration is not stored in the storage device.